class_docstr: Return conditioned Behavior cloning agent.
docstr: Initializes the agent and constructs the necessary components.
  
  Args:
    num_actions: int, number of actions the agent can take at any state.
    replay_data_dir: str, log Directory from which to load the replay buffer.
    summary_writer: SummaryWriter object for outputting training statistics.
    use_tfds: Whether to use tfds replay buffer.
docstr_tok: ["Initializes","the","agent","and","constructs","the","necessary","components",".","Args",":","num_actions",":","int",",","number","of","actions","the","agent","can","take","at","any","state",".","replay_data_dir",":","str",",","log","Directory","from","which","to","load","the","replay","buffer",".","summary_writer",":","SummaryWriter","object","for","outputting","training","statistics",".","use_tfds",":","Whether","to","use","tfds","replay","buffer","."]
code: def __init__(
      self,
      num_actions,
      replay_data_dir,
      summary_writer=None,
      use_tfds=True,
  ):
      """Initializes the agent and constructs the necessary components.
  
      Args:
        num_actions: int, number of actions the agent can take at any state.
        replay_data_dir: str, log Directory from which to load the replay buffer.
        summary_writer: SummaryWriter object for outputting training statistics.
        use_tfds: Whether to use tfds replay buffer.
      """
  
      logging.info("Creating JaxReturnConditionedBCAgent ..")
      super().__init__(
          num_actions,
          replay_data_dir=replay_data_dir,
          bc_coefficient=1.0,
          td_coefficient=0.0,
          use_tfds=use_tfds,
          summary_writer=summary_writer,
      )
      self._create_wrapped_network()
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 1 to create a new JaxReturnConditionedBCAgent object from inherited base classes: offline_rainbow_agent with example: obj = JaxReturnConditionedBCAgent(num_actions, replay_data_dir, summary_writer, use_tfds)